Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa

Metelka, Václav; Baratoux, Lenka; Jessell, Mark; Barth, Andreas; Ježek, Josef; Naba, Séta

doi:10.1016/j.rse.2017.08.004

Cited by 43 publications

(21 citation statements)

References 63 publications

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“…Farther away, the roughness at the intermediate scale increases and reaches typical signatures for lateritic plateaus encountered in West Africa (Metelka et al. ). The rough area corresponding to the Birimian metavolcanics in the southeastern part of the crater structure is not discussed here.…”

Section: Morphological Analysis Of the Bosumtwi Impact Cratermentioning

confidence: 77%

Bosumtwi impact structure, Ghana: Evidence for fluidized emplacement of the ejecta

Baratoux

Niang

Reimold

et al. 2019

Meteorit & Planetary Scien

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The about 10.5 km diameter Bosumtwi impact crater is one of the youngest large impact structures on Earth. The crater rim is readily noticed on topographic maps or in satellite imagery. It defines a circular basin filled by water (Lake Bosumtwi) and lacustrine sediments. The morphology of this impact structure is also characterized by a circular plateau extending beyond the rim and up to 9–10 km from the center of the crater (about 2 crater radii). This feature comprises a shallow ring depression, also described as an annular moat, and a subdued circular ridge at its outer edge. The origin of this outermost feature could so far not be elucidated based on remote sensing data only. Our approach combines detailed topographic analysis, including roughness mapping, with airborne radiometric surveys (mapping near‐surface K, Th, U concentrations) and field observations. This provides evidence that the moat and outer ring are features inherited from the impact event and represent the partially eroded ejecta layer of the Bosumtwi impact structure. The characteristics of the outer ridge indicate that ejecta emplacement was not purely ballistic but requires ejecta fluidization and surface flow. The setting of Bosumtwi ejecta can therefore be considered as a terrestrial analog for rampart craters, which are common on Mars and Venus, and also found on icy bodies of the outer solar system (e.g., Ganymede, Europa, Dione, Tethys, and Charon). Future studies at Bosumtwi may therefore help to elucidate the mechanism of formation of rampart craters.

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Section: Morphological Analysis Of the Bosumtwi Impact Cratermentioning

confidence: 77%

Bosumtwi impact structure, Ghana: Evidence for fluidized emplacement of the ejecta

Baratoux

Niang

Reimold

et al. 2019

Meteorit & Planetary Scien

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“…Satellite remote-sensing data and advances in digital image processing (DIP) techniques provided a new impulse to the development of lithological mapping. Spectral data from space and airborne sensors were widely applied to geological mapping, including lithological discrimination [1][2][3][4][5][6][7][8], structural mapping [9], hydrothermal alteration [10][11][12], and economic mineral deposits [13][14][15][16][17]. Because of their cost effectiveness, especially in mapping inaccessible areas [4,[18][19][20] and in the production of small-scale maps, remote-sensing methods provide a good alternative to traditional field work [21].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Bachri

Hakdaoui

Raji

et al. 2019

IJGI

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Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

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“…Although it is not new to use remote sensing technique for lithological classification in geological investigation [2,22,23], many studies are limited, due to the coarse spatial/spectral resolutions of multispectral data, causing difficulties in accurately classifying rock units [22]. As a solution, multiple ancillary data with texture information, such as airborne geophysical data [24], DEM [25], and geomorphic feature [2], can be integrated with multispectral imagery for improved lithological classifications [22]. However, the integration of multispectral data with different bandpasses for lithological classification is of little concern in previous literature.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

Cheng

Tang

et al. 2018

Remote Sensing

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As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification.

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Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa

Cited by 43 publications

References 63 publications

Bosumtwi impact structure, Ghana: Evidence for fluidized emplacement of the ejecta

Bosumtwi impact structure, Ghana: Evidence for fluidized emplacement of the ejecta

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

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